Developing a prognostic risk model based on circulating tumor cell genes to predict prognosis and provide potential therapeutic strategies in colorectal cancer.

IF 1.5 4区医学 Q4 ONCOLOGY

Translational cancer research Pub Date : 2025-05-30 Epub Date: 2025-05-16 DOI:10.21037/tcr-2024-2268

Yupeng Zheng, Mian Yang, Hongyi Yi, Tao Peng, Jiaze Sun, Jiazi Yu

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引用次数: 0

Abstract

Background: Colorectal cancer (CRC) is a major cause of cancer-related deaths worldwide. Understanding the genetic and molecular alterations in CRC can improve patient outcomes. Circulating tumor cells (CTCs) are crucial in cancer metastasis and progression. Analyzing the differentially expressed genes (DEGs) between CTCs and CRC may provide us with new therapeutic strategies. Therefore, this study aims to analyze these DEGs to construct a prognostic risk model that predicts the outcomes of CRC patients and guides clinical treatment.

Methods: We analyzed The Cancer Genome Atlas (TCGA) database to identify 1,727 DEGs between CRC and normal samples, and GSE82198 data to find 3,564 DEGs between CTCs and primary CRC samples. Using enrichment analysis, least absolute shrinkage and selection operator (LASSO) regression, and stepwise Cox regression, we derived eight model genes to construct a prognostic risk model. Various algorithms were employed in the immune microenvironment analysis. Integrating clinical factors with risk grouping, we developed a nomogram. We assessed chemotherapy sensitivity and epithelial-mesenchymal transition (EMT) scores in high-/low-risk groups and explored model gene expression at the single-cell level.

Results: We constructed a prognostic risk model for CRC based on eight DEGs of CTCs. The model effectively predicted treatment outcomes and correlated closely with actual prognosis. Through immune microenvironment analysis, we revealed differences in immune cell infiltration and checkpoint gene expression among different risk groups. Moreover, patients in the high-risk group showed higher sensitivity to chemotherapy drugs compared to those in the low-risk group.

Conclusions: The prognosis model based on CTCs' DEGs can effectively predict patient outcomes, facilitating precision treatment for patients. This model holds significant guiding implications for immunotherapy and chemotherapy in CRC, offering potential strategies for the clinical treatment of CRC.

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建立基于循环肿瘤细胞基因的预后风险模型，预测结直肠癌的预后并提供潜在的治疗策略。

背景：结直肠癌（CRC）是世界范围内癌症相关死亡的主要原因。了解结直肠癌的遗传和分子改变可以改善患者的预后。循环肿瘤细胞（CTCs）在肿瘤转移和进展中起着至关重要的作用。分析ctc和CRC之间的差异表达基因（DEGs）可能为我们提供新的治疗策略。因此，本研究旨在对这些deg进行分析，构建预测结直肠癌患者预后并指导临床治疗的预后风险模型。方法：通过分析美国癌症基因组图谱（Cancer Genome Atlas， TCGA）数据库和GSE82198数据库，分别鉴定出1727个结直肠癌与正常样本之间的基因差异，以及3564个结直肠癌与原发性结直肠癌样本之间的基因差异。通过富集分析、最小绝对收缩和选择算子（LASSO）回归和逐步Cox回归，我们导出了8个模型基因来构建预后风险模型。免疫微环境分析采用了多种算法。将临床因素与风险分组相结合，我们开发了一个nomogram。我们评估了高/低风险组的化疗敏感性和上皮-间质转化（EMT）评分，并探索了单细胞水平的模型基因表达。结果：我们基于ctc的8个deg构建了CRC的预后风险模型。该模型能有效预测治疗结果，与实际预后密切相关。通过免疫微环境分析，我们揭示了不同风险人群免疫细胞浸润和检查点基因表达的差异。此外，与低危组相比，高危组患者对化疗药物的敏感性更高。结论：基于ctc deg的预后模型能有效预测患者预后，便于患者的精准治疗。该模型对结直肠癌的免疫治疗和化疗具有重要的指导意义，为结直肠癌的临床治疗提供潜在的策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational cancer research ONCOLOGY-

CiteScore

2.10

自引率

0.00%

发文量

252

期刊介绍： Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.